1. Field of the Invention
The present invention relates to volumetric image data characterization, and more particularly to a system and method for treating a scale selection problem in the anisotropic scale-space.
2. Description of Related Art
Gaussian scale-space theory offers a general paradigm for analyzing various image features of arbitrary size. One of its useful attributes is the maximum-over-scales property of the γ-normalized derivatives. Under the maximum-over-scales approach, the characteristic scale of a feature at the spatial local maximum location corresponds to the bandwidth of the Gaussian kernel that provides the local maximum of the normalized derivatives over the varying bandwidths at the location. This is a proposed solution to the general scale selection problem: given a set of analysis scales (bandwidths), find the analysis scale that provides the best estimate of the local feature's scale or other properties. The theory has been studied extensively and applied to various problems. However, the main focuses have been on the scale-space functions that model either the isotropic homogeneous or anisotropic inhomogeneous diffusion processes.
Therefore, a need exists for a system and method for treating a scale selection problem in the anisotropic scale-space